import pandas as pd
import statsmodels.api as sm # type: ignore
from sklearn.model_selection import train_test_split
from sqlalchemy import create_engine
from sklearn.metrics import r2_score, mean_absolute_error
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pymysql

db_config = {
    'host': '127.0.0.1',
    'user': 'root',
    'password': 'root',
    'port': 3306,
    'charset':'utf8',
    'database': 'lyf'  # 替换为实际数据库名
}

engine =create_engine(
    f"mysql+pymysql://{db_config['user']}:{db_config['password']}@{db_config['host']}:{db_config['port']}/{db_config['database']}?{db_config['charset']}"
)
conn=pymysql.connect(**db_config)
chunk_size=10000
df=pd.read_sql_query(
    """
    SELECT d.*FROM date_1 d WHERE d.trade_date BETWEEN '2023-01-01' and '2024-01-01' and d.ts_code = '000001.SZ'
    """,
    conn,
    chunksize=chunk_size
)
df1=pd.concat(df,ignore_index=True)

df1['zd_closes']=round(((df1['closes'] - df1['closes'].shift(1))/df1['closes'].shift(1)),2)
print(df1.head)
